Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
Reads0
Chats0
TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.Abstract:
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.read more
Citations
More filters
Proceedings ArticleDOI
Approximating large scale arbitrary unitaries with integrated multimode interferometers
Matthew van Niekerk,Jeffrey A. Steidle,Gregory A. Howland,Michael L. Fanto,Michael L. Fanto,Nicholas Soures,Fatima Tuz Zohora,Dhireesha Kudithipudi,Stefan F. Preble +8 more
TL;DR: This paper investigates the active control of MMIs and their suitability for approximating traditionally used unitary circuits and proposes to considerably reduce the footprint by using multimode interference (MMI) devices.
Journal ArticleDOI
Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits
TL;DR: A pruned high-speed and energy-efficient optical backpropagation (BP) neural network using the micro-ring resonator (MRR) banks, as the core of the weight matrix operation, and tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization.
Journal ArticleDOI
Knowledge distillation circumvents nonlinearity for optical convolutional neural networks
TL;DR: In this article , a knowledge distillation (KD) approach is proposed to circumvent the need for nonlinear layers between the convolutional layers and successfully train such networks, which can achieve performance that easily surpasses the standard linear version of a CNN and could approach the performance of the nonlinear network.
Proceedings ArticleDOI
Demonstration of 3±0.12 dB Power Splitting over 145 nm Optical Bandwidth in a 31-μm Long 3-dB Rapid Adiabatic Coupler
TL;DR: This work experimentally validate the rapid adiabatic coupling (RAC) concept and demonstrates 50±1.4% (3±0.12dB) power splitting over a record 145 nm bandwidth from either port of a 31μm-long, 2×2 coupler.
Journal ArticleDOI
Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network
Ruiyang Chen,Yingjie Li,Minhan Lou,Jichao Fan,Yingheng Tang,Berardi Sensale-Rodriguez,Cunxi Yu,Weilu Gao +7 more
TL;DR: A large-scale, cost-effective, complex-valued, and reconfigurable diffractive all-optical neural networks system in the visible range based on cascaded transmissive twisted nematic liquid crystal spatial light modulators and physics-aware adversarial attacks are demonstrated.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.